We compare the proposed method with state-of-the-art methods including two SISR methods i.e., EDSR [1]}, RCAN [2]}), and seven LFSR methods i.e., LFBM5D [3]}, GB [4]}, resLF [5]}, LFSSR [6]}, LF-InterNet [7]}, ATO [8]} and LF-DFnet [9]}. Bicubic interpolation was included as baselines. We have retrained all learning-based methods [1]}, [2]}, [5]}, [6]}, [7]}, [8]}, [9]} on the same training datasets as ours. The peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) [17]} were used for quantitative evaluation.
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